Search Results (596)

Thermodynamic entropy generation quantifies irreversibility in energy conversion processes, providing rigorous thermodynamic foundations for optimizing efficiency and sustainability in thermal and energy systems. This critical review synthesizes advances in computational entropy modeling across numerical methods, optimization strategies, and sustainable energy applications. Computational fluid dynamics, finite element methods, and lattice Boltzmann methods enable spatially resolved entropy analysis in convective, conjugate, and microscale systems, but exhibit varying maturity levels and accuracy–cost trade-offs. The minimization of entropy generation and the integration of artificial intelligence demonstrate quantifiable performance improvements in heat exchangers, renewable energy systems, and smart grids, with reported efficiency gains of 15 to 39% in specific applications under controlled conditions. While overall performance depends critically on system scale, operating regime, and baseline configuration, persistent limitations still constrain practical deployment. Systematic conflation between thermodynamic entropy (quantifying physical irreversibility) and information entropy (measuring statistical uncertainty) leads to inappropriate method selection; validation challenges arise from entropy’s status as a non-directly-measurable state function; high-order maximum entropy models achieve superior uncertainty quantification but require prohibitive computational resources; and standardized benchmarking protocols remain absent. Research fragmentation across thermodynamics, information theory, and machine learning communities limits integrated frameworks capable of addressing multi-scale, transient, multiphysics systems. This review provides structured, cross-method, application-aware synthesis identifying where computational entropy modeling achieves industrial readiness versus research-stage development, offering forward-looking insights on physics-informed machine learning, unified theoretical frameworks, and real-time entropy-aware control as critical directions for advancing sustainable energy system design. Full article

With the development of aerospace technology, hypersonic flight vehicles are evolving towards larger size, lighter weight, and higher performance. Their cross-domain maneuverability and extreme flight environment led to the rigid–flexible coupling effect and became the core bottleneck restricting performance improvement, seriously affecting flight stability and control accuracy. This paper systematically reviews the research status in the field of control for high-speed rigid–flexible coupling aircraft and conducts a review focusing on two core aspects: dynamic modeling and control strategies. In terms of modeling, the modeling framework based on the average shafting, the nondeformed aircraft fixed-coordinate system, and the transient coordinate system is summarized. In addition, the dedicated modeling methods for key issues, such as elastic mode coupling and liquid sloshing in the fuel tank, are also presented. The research progress and challenges of multi-physical field (thermal–structure–control, fluid–structure–control) coupling modeling are analyzed. In terms of control strategies, the development and application of linear control, nonlinear control (robust control, sliding mode variable structure control), and intelligent control (model predictive control, neural network control, prescribed performance control) are elaborated. Meanwhile, it is pointed out that the current research has limitations, such as insufficient characterization of multi-physical field coupling, neglect of the closed-loop coupling characteristics of elastic vibration, and lack of adaptability to special working conditions. Finally, the relevant research directions are prospected according to the priority of “near-term engineering requirements–long-term frontier exploration”, providing Refs. for the breakthrough of the rigid–flexible coupling control technology of the new-generation high-speed aircraft. Full article

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented separately, even though they share common unsteady vortex dynamics. Accordingly, we adopt a unified unsteady-aerodynamic perspective to relate propulsion and energy-extraction regimes within a common framework and to clarify their operational duality. Within this unified framework, the feathering parameter provides a theoretical delimiter between momentum transfer and kinetic energy extraction. A critical analysis of experimental foundations demonstrates that while passive structural flexibility enhances propulsive thrust via favorable wake interactions, synchronization mismatches between deformation and peak hydrodynamic loading constrain its benefits in power generation. This review extends the analysis to complex and non-homogeneous environments and identifies that density stratification fundamentally alters the hydrodynamic performance. Specifically, resonant interactions with the natural Brunt–Väisälä frequency of the fluid shift the optimal kinematic regimes. The present study also surveys computational methodologies and highlights a paradigm shift from traditional parametric sweeps to high-fidelity three-dimensional (3D) Large-Eddy Simulations (LESs) and Deep Reinforcement Learning (DRL) to resolve finite-span vortex interconnectivities. Finally, this review outlines the critical pathways for future research. To bridge the gap between computational idealization and physical reality, the findings suggest that future systems prioritize tunable stiffness mechanisms, multi-phase environmental modeling, and artificial intelligence (AI)-driven digital twin frameworks for real-time adaptation. Full article

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article

During the trial mining process, ground horizontal pipes are prone to generating hydrates due to pressure and temperature changes, leading to ice blockage. Hydrate inhibitors are usually added on-site to prevent freezing blockage. However, existing addition methods have limitations, including poor real-time performance, insufficient accuracy in the addition amount, and dependence on manual adjustment. In view of this, this paper aims to develop models to predict the throttling pressure and temperature for horizontal ground pipes, and to indicate the amount of ethylene glycol needed to prevent freezing blockage, thereby laying the foundation for accurate, real-time prediction of fluid pressure and temperature and for controlling the addition amount. By integrating data-driven technologies and mechanism models, this study developed intelligent prediction systems for ground horizontal pipe throttling pressure and temperature, and for suppression of freeze-blocking ethylene glycol addition. First, a three-phase throttling mechanism model for oil, gas, and water is established using the energy conservation equation to accurately predict the pressure and temperature at the throttling points along the process. At the same time, HYSYS software is used to simulate various operating conditions and to fit the ethylene glycol addition amount prediction model. Finally, edge computing equipment is integrated to enable real-time data collection, prediction, and dynamic adjustment and optimization. The field measurement data of Well A showed that the model’s prediction error of pressure and temperature before and after throttling is less than 6%, and the prediction error of the ethylene glycol addition amount is less than 5%, which provides key technical support for safe and efficient operation of the trial mining process as well as for cost reduction and efficiency improvement. Full article

Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities to augment POCUS by supporting image acquisition, interpretation, and quantitative analysis. This narrative review synthesizes current evidence on AI-enhanced POCUS applications in emergency care, encompassing trauma, non-traumatic emergencies, integrated workflows, resource-limited settings, and education and training. Across trauma settings, AI-assisted POCUS has demonstrated promising performance for automated detection of pneumothorax, hemothorax, and free intraperitoneal fluid, supporting standardized eFAST examinations and rapid triage. In non-traumatic emergencies, AI-enabled cardiovascular, pulmonary, and abdominal applications provide automated measurements and pattern recognition that can approach expert-level performance when image quality is adequate. Integrated AI–POCUS systems and educational tools further highlight the potential to expand ultrasound access, support non-expert users, and standardize training. Nevertheless, important limitations persist, including limited generalizability, dataset bias, device heterogeneity, and uncertain impact on clinical decision-making and patient outcomes. In conclusion, AI-enhanced POCUS is transitioning from proof-of-concept toward early clinical integration in emergency medicine. While current evidence supports its role as a decision-support tool that may enhance consistency and efficiency, widespread adoption will require prospective multicentre validation, development of representative POCUS-specific datasets, vendor-agnostic solutions, and alignment with clinical, ethical, and regulatory frameworks. Full article

Accurate measurement of wall shear stress (WSS) is essential for both fundamental and applied fluid dynamics, where it governs boundary-layer behavior, drag generation, and the performance of flow-control systems. Yet, existing WSS sensing methods remain limited by low spatial resolution, complex instrumentation, or the need for user-dependent calibration. This work introduces a method based on artificial intelligence (AI) and Oil-Film Interferometry, referred to as AI-OFI, that transforms a classical optical technique into an automated and sensor-like platform for local WSS detection. The method combines the non-intrusive precision of Oil-Film Interferometry with modern deep-learning tools to achieve fast and fully autonomous data interpretation. Interference patterns generated by a thinning oil film are first segmented in real time using a YOLO-based object detection network and subsequently analyzed through a modified VGG16 regression model to estimate the local film thickness and the corresponding WSS. A smart interrogation-window selection algorithm, based on 2D Fourier analysis, ensures robust fringe detection under varying illumination and oil distribution conditions. The AI-OFI system was validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE), showing excellent agreement with reference pressure-drop measurements and conventional OFI, with an average deviation below 5%. The proposed framework enables reliable, real-time, and operator-independent wall shear stress sensing, representing a significant step toward next-generation optical sensors for aerodynamic and industrial flow applications. Full article

During takeoff and landing, aircraft operate in a variety of situations, posing significant challenges to landing gear systems. Passive hydraulic–pneumatic dampers are commonly used in conventional landing gear to absorb impact energy and reduce vibration. However, due to their fixed damping characteristics and inability to adjust to changing operating conditions, these passive systems have several limitations. Recent research has focused on creating intelligent landing gear systems with magnetic dampers (MR) to overcome these limitations. By changing the magnetic field acting on the MR fluid, MR dampers provide semi-active control of the landing gear dynamics and adjust the damping force in real time. This flexibility reduces structural load during landing, increases riding comfort, and improves energy absorption efficiency. This study examines the current state of MR damper application for aircraft landing gear. The review categorizes current control techniques and highlights the structural integration of MR dampers in landing gear assemblies. Purpose: The magnetorheological (MR) damper has become a promising semiactive system to replace the conventional passive damper in aircraft landing gear. However, the mechanical structure and control strategy of the MR damper must be designed to be suitable for aircraft landing gear applications. Methods: Researchers have explored the potential structure designed, the mathematical model of the MR landing gear system, and the control algorithm that was developed for aircraft landing gear applications. Results: According to the mathematical model of the MR damper, three types of models, which are pseudo-static models, parametric models, and unparameterized models, are detailed with their application. Based on these mathematical models, many control algorithms were studied, from classical control, such as PID and skyhook control, to modern control, such as intelligent control and SMC control. Full article

With advancements in simulation technology, fluid–thermal interaction (FTI) has become a vital tool in machinery powertrain development. Traditional engine cooling systems, with mechanically coupled components like water pumps and fans, lack adaptive cooling control. Electronic cooling systems, however, use variable-speed components to enhance performance. Combining FTI simulations with intelligent optimization algorithms offers a novel approach to designing control strategies for these systems. This study establishes a multi-objective optimization model for pump and fan speed control in electronic cooling systems. Using MATLAB/Simulink 2018 and Fluent 2022R1, co-simulations were performed, and an elite-strategy-based NSGA-II algorithm was implemented. Different weights were assigned to optimization objectives based on engine performance requirements. The results provide fitted functions for heat exchange capacity and cylinder liner temperature versus flow rates, along with optimal solutions for a 65 kW engine under three weight configurations. These findings support control strategy design and demonstrate the integration of FTI with genetic algorithms. Full article

Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, which poses significant challenges for integration into industrial production processes. Three-dimensional printing technology provides a cost-effective alternative to traditional fabrication methods, offering particular benefits for the development of low-cost designs for detecting liquid properties. In this work, we present a sensor system for assessing biofuel solutions. The presented device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery and resolution. This system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and $1.48\times {10}^{-2}$ mPa·s for viscosity, and 0.0485% and $1.9\times {10}^{-4}$ g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan and accelerate data acquisition. The resulting system is compact, precise and applicable to diverse industrial liquids. Full article

Along with the growing demands for personalized medicine and public health surveillance, diagnostic technologies capable of rapid and accurate pathogen nucleic acid testing in home settings are becoming increasingly crucial. Paper-based microfluidic chips (μPADs) have emerged as a potential core platform for enabling molecular testing at home, owing to their advantages of low cost, portability, and independence from complex instrumentation. However, significant challenges remain in the current μPADs systems regarding nucleic acid extraction efficiency, isothermal amplification stability, and signal readout standardization, which hinder their practical and large-scale application. This review systematically summarizes recent research progress in μPADs for home-based nucleic acid testing from four key aspects: extraction–amplification–detection system integration, with a particular focus on the synergistic effects and development trends of critical technologies such as material engineering, fluid control, signal transduction, and intelligent readout. We further analyze typical application cases of this technology in the rapid screening of infectious disease. Promising optimization pathways are proposed, focusing on standardized manufacturing, cold-chain-independent storage, and AI-assisted result interpretation, aiming to provide a feasible framework and forward-looking perspectives for constructing home-based molecular diagnostic systems. Full article

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models. Full article

Three-dimensional (3D) printing has transformed the development of microneedle arrays (MNAs) by enabling exceptional control over their geometry, distribution, materials, and functionality in a single-step, customizable process. This review represents a design-centric framework that organizes recent advancements in four interconnected levers: (i) individual microneedle (MN) geometry and size; (ii) patch-level MN distribution and multi-array architectures; (iii) computer-aided design (CAD), finite element analysis (FEA), computational fluid dynamics (CFD), and artificial intelligence/machine learning (AI/ML)-driven optimization; and (iv) manufacturing constraints and emerging solutions for scalability and reproducibility. Outcomes show that small changes in the radius of the MN’s tip, the MN’s aspect ratio, the MN’s internal lattice architecture, and the spacing of the array can dramatically influence their insertion force, mechanical reliability, payload capacity, and therapeutic coverage. Now, digital tools can bridge the design and experimental outcomes, while novel morphologies, hybrid materials, and theranostic integrations are expanding the clinical potential of MNs. The remaining challenges, resolution-versus-throughput trade-offs, biocompatibility, batch-to-batch consistency, and lack of testing standardization are examined alongside promising directions in high-throughput 3D printing, stimuli-responsive materials, and closed-loop systems. Finally, rational, model-guided design strategies are positioning 3D-printed MNAs as versatile platforms for painless, patient-specific drug delivery, diagnostics, and personalized medicine. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems. Full article